High dimensional (hidi) radio environment characterization and representation

ABSTRACT

A device comprises a memory that stores instructions executed by one or more processors to obtain a plurality of received signals transmitted by a user equipment from a plurality of antenna elements in a cellular network. A plurality of received signals transmitted by a user equipment from a plurality of antenna elements in a cellular network are obtained, and a plurality of complex channel values are calculated in an angle domain for a horizontal arrival angle and a vertical arrival angle per a received ray in a plurality of received rays in response to the plurality of received signals. An expected value of the plurality of channel values are calculated to obtain a power value for the horizontal arrival angle and vertical arrival angle having a time delay per the received ray.

CLAIM FOR PRIORITY

This application is a Continuation-in-Part of and claims the benefit ofpriority to U.S. patent application Ser. No. 15/674,552, issuing as U.S.Pat. No. 10,020,922, filed Aug. 11, 2017, which claims priority to U.S.patent application Ser. No. 15/382,325, now issued as U.S. Pat. No.9,768,928, filed Dec. 16, 2016, the entire contents of which are herebyincorporated by reference.

FIELD

The present technology generally relates to obtaining a high dimensional(HiDi) radio environment (channel) representation, in for example, acellular network using orthogonal frequency-division multiplexing (OFDM)signals.

BACKGROUND

Knowing the radio environment around a cellular base station (BS) mayhelp improve the performance of a BS. For example, user handover, radioresource management, link adaptation and channel estimation may beimproved by knowing the radio environment, or signal characterizationsof received signals from various locations near the BS.

Some BSs may be located in rural areas with a generally flat topographywhere received signals from user equipment (UE) located at variouspositions may have similar received signal characteristics. Other BSsmay be located in an urban area with clusters of buildings of variousshapes and sizes along with open areas, such as parks or bodies ofwater. In this type of radio environment, received signals from UEs atvarious locations may have very different signal characteristics, orchannel representations.

Drive tests may be used to obtain a radio environment maps for variousBSs. However, these drive tests may be costly. Further, a radioenvironment map may consist of one dimensional scalars, such as receivedpower or signal-to-noise (SNR) values for particular locations. Theseone dimension map representations may not be good enough to capture theuniqueness of radio characteristics at a particular location.

SUMMARY

According to one aspect of the present disclosure, there is a device,comprising a non-transitory memory storing instructions; and one or moreprocessors in communication with the non-transitory memory, wherein theone or more processors execute the instructions to: obtain a pluralityof received signals transmitted by a user equipment from a plurality ofantenna elements in a cellular network; calculate a plurality of complexchannel values in an angle domain for a horizontal arrival angle and avertical arrival angle per a received ray in a plurality of receivedrays in response to the plurality of received signals, and calculate anexpected value of the plurality of channel values to obtain a powervalue for the horizontal arrival angle and vertical arrival angle havinga time delay per the received ray.

Optionally, in any of the preceding aspects, the plurality of complexchannel values for a horizontal arrival angle and a vertical arrivalangle per a received ray having a delay per received ray is compensatedwith a time-offset estimate for the plurality of complex channel values.

Optionally, in any of the preceding aspects, a time-offset estimate forthe plurality of complex channel values is calculated as the smallesttime delay of the plurality of channel values in time domain.

Optionally, in any of the preceding aspects, the obtain the plurality ofreceived signals include obtain a plurality of orthogonalfrequency-division multiplexing (OFDM) signals and the plurality ofantenna elements are included in a multiple-input and multiple-output(MIMO) antenna.

Optionally, in any of the preceding aspects, the plurality of OFDMsignals include a plurality of sounding reference signals in a pluralityof subcarrier signals of a resource block transmitted by the userequipment.

Optionally, in any of the preceding aspects, the calculate a pluralityof complex channel values in the angle domain includes: obtain an angledomain-based channel estimation and providing the plurality of receivedsignals to the angle domain-based channel to obtain the plurality ofcomplex channel values.

Optionally, in any of the preceding aspects, the obtain the angledomain-based channel estimation includes using an array signalprocessing that includes one of N-point discrete Fourier transform (DFT)steering, minimum variance distortionless response (MVDR) and multiplesignal classification (MUSIC).

Optionally, in any of the preceding aspects, the one or more processorsexecute the instructions to: obtain a geographical location for the userequipment and store the power value, the horizontal arrival angle,vertical arrival angle and a second time delay per the received ray forthe geographical location in another non-transitory memory.

Optionally, in any of the preceding aspects, the device is included in abase station having the plurality of antenna elements for communicatingwith the user equipment in the cellular network, wherein the one or moreprocessors execute the instructions to: retrieve the power value, thehorizontal arrival angle, vertical arrival angle and a second time delayper the received ray for the geographical location to use in one of userhandover, radio resource management, link adaptation and channelestimation in the base station.

According to one aspect of the present disclosure, there is acomputer-implemented method for a base station having a plurality ofantennas to communicate with a user equipment in a cellular network,comprising receiving a plurality of subcarrier signals in an orthogonalfrequency-division multiplexing (OFDM) signal at a plurality of timeintervals transmitted by the user equipment from the plurality ofantennas; calculating the channel values in a frequency domain inresponse to the plurality of subcarrier signals; vectorizing the channelin the time domain to obtain a vector of the channel in the time domain;and correlating the vector to obtain a spatial-time correlation.

According to one aspect of the present disclosure, there is anon-transitory computer-readable medium storing computer instructions,that when executed by one or more processors, cause one or moreprocessors to: receiving a plurality of subcarrier signals in anorthogonal frequency-division multiplexing (OFDM) signal at a pluralityof time intervals transmitted by a user equipment from a plurality ofantennas at a base station in a cellular network; calculating channelvalues in a frequency domain in response to the plurality of subcarriersignals; vectorizing the channel in frequency domain to obtain a vectorof the channel in the frequency domain; and correlating the vector toobtain a spatial-frequency correlation.

According to one aspect of the present disclosure, there is a device,comprising a non-transitory memory storing instructions; and one or moreprocessors in communication with the non-transitory memory, wherein theone or more processors execute the instructions to: obtain a pluralityof received signals transmitted by a user equipment from a plurality ofantenna elements in a cellular network; calculate a plurality of complexchannel values as raw channel estimate in a frequency domain in responseto the plurality of subcarrier signals; and filter the raw channelestimates with power angle delay profiles, spatial time correlation, orspatial frequency correlation to obtain improved channel estimates.

Optionally, in any of the preceding aspects, the power angle delayprofiles are a plurality of received rays with each ray represented by apower value, a horizontal arrival angle and vertical arrival angle, atime delay per the received ray that are obtained by the measurement ofthe channel values in previous time slots.

Optionally, in any of the preceding aspects, wherein the spatial timecorrelation is the correlating of the vectorized channel in spatial andtime domain from the channel values in previous time slots.

Optionally, in any of the preceding aspects, the spatial frequencycorrelation is the correlating of the vectorized channel in spatial andfrequency domain from the channel values in previous time slots.

According to one aspect of the present disclosure, there is acomputer-implemented method for a base station having a plurality ofantennas to communicate with a user equipment in a cellular network,comprising receiving a plurality of subcarrier signals in an orthogonalfrequency-division multiplexing (OFDM) signal at a plurality of timeintervals transmitted by the user equipment from the plurality ofantennas; calculating the channel values in a frequency domain inresponse to the plurality of subcarrier signals; transforming thechannel values to the time domain; generating a pseudo pilot sequence inthe time domain; obtaining the convolutional signal between the channelvalues and the pseudo pilot sequence for each antennal; vectorizing theconvolutional signal in the time domain for all antennas to obtain avector of the convolutional signal in the spatial time domain; andcorrelating the vector to obtain a spatial-time correlation.

Optionally, in any of the preceding aspects, the pseudo pilot sequenceconsists of a plurality of pilot symbols.

Optionally, in any of the preceding aspects, the pseudo pilot sequencecan be a predefined sequence.

Optionally, in any of the preceding aspects, the pilot symbols of thepseudo pilot sequence have equal power.

According to one aspect of the present disclosure, there is acomputer-implemented method for a base station having a plurality ofantennas to communicate with a user equipment in a cellular network,comprising receiving a plurality of subcarrier signals in an orthogonalfrequency-division multiplexing (OFDM) signal at a plurality of timeintervals transmitted by the user equipment from the plurality ofantennas; calculating the channel values in a frequency domain inresponse to the plurality of subcarrier signals; generating a pseudopilot sequence in the frequency domain; obtaining the multiplicationsignal by multiplying each symbol of the pseudo pilot sequence in thefrequency domain to the channel value on the corresponding subcarrier;transforming the multiplication signal in the frequency domain to thetime domain; vectorizing the transformed multiplication signal in timedomain for all antennas to obtain a vector of the signal in the spatialtime domain; and correlating the vector to obtain a spatial-timecorrelation.

Optionally, in any of the preceding aspects, the pseudo pilot sequenceconsists of a plurality of pilot symbols.

Optionally, in any of the preceding aspects, the pseudo pilot sequencecan be a predefined sequence.

Optionally, in any of the preceding aspects, the pseudo pilot sequencecan be generated in time domain and transformed to the frequency domain.

Optionally, in any of the preceding aspects, the pilot symbols of thepseudo pilot sequence in time domain have equal power.

This Summary is provided to introduce a selection of concepts in asimplified form that are further described below in the DetailedDescription. This Summary and/or headings are not intended to identifykey features or essential features of the claimed subject matter, nor isit intended to be used as an aid in determining the scope of the claimedsubject matter. The claimed subject matter is not limited toimplementations that solve any or all disadvantages noted in theBackground.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates a cellular network having multiple cells according toembodiments of the present technology.

FIG. 2 illustrates power angle delay profiles according to embodimentsof the present technology.

FIG. 3A is a block diagram that illustrates obtaining power angle delayprofiles according to embodiments of the present technology.

FIG. 3B illustrates a time-offset estimation according to embodiments ofthe present technology.

FIG. 3C illustrates a frequency-offset estimation according toembodiments of the present technology.

FIGS. 3D-E illustrates a relationship between power angle delay profilesand frequency correlations according to embodiments of the presenttechnology.

FIG. 4A is a block diagram that illustrates obtaining spatial-timecorrelations according to embodiments of the present technology.

FIG. 4B illustrates the relationship between the PADP and spatial-timecorrelations as well as radio environment representations withspatial-time correlations of the channel state information according toembodiments of the present technology.

FIG. 4C is a block diagram 400C that illustrates obtaining another typeof spatial-time correlations for UEs transmitting at particulargeographical locations in a cell according to embodiments of the presenttechnology.

FIG. 4D is a block diagram 400D that illustrates obtaining another typeof spatial-time correlations for UEs transmitting at particulargeographical locations in a cell according to embodiments of the presenttechnology.

FIGS. 4E-F illustrates frequency correlation using different subcarrierspacing according to embodiments of the present technology.

FIG. 5A is a block diagram that illustrates obtaining spatial-frequencycorrelations according to embodiments of the present technology.

FIG. 5B illustrates the relationship between the PADP andspatial-frequency correlations as well as radio environmentrepresentations with spatial-frequency correlations of the channel stateinformation according to embodiments of the present technology.

FIG. 5C illustrates spatial-frequency correlation using differentantenna elements according to embodiments of the present technology.

FIG. 5D illustrates frequency correlation using different subcarrierspacing and wideband averaging according to embodiments of the presenttechnology.

FIG. 6 is a flowchart that illustrates a method for obtaining a powerangle delay profile according to embodiments of the present technology.

FIGS. 7A-B is a flowchart that illustrates a method for obtaining powerestimates by spatial-temporal correlation according to embodiments ofthe present technology.

FIG. 8 is a flowchart that illustrates a method for obtaining powerestimates using spatial-frequency correlation according to embodimentsof the present technology.

FIG. 9 is a block diagram that illustrates a hardware architectureaccording to embodiments of the present technology.

FIG. 10 is a block diagram that illustrates a software architectureaccording to embodiments of the present technology.

Corresponding numerals and symbols in the different figures generallyrefer to corresponding parts unless otherwise indicated. Symbols in boldand/or bracketed may represent a set of information and/or matrix ofinformation unless clearly indicated otherwise in the figures and/ordetailed description. The figures are drawn to clearly illustrate therelevant aspects of the embodiments and are not necessarily drawn toscale.

DETAILED DESCRIPTION

The present technology generally relates to obtaining a high dimensional(HiDi) radio environment (channel) representation, in for example, acellular network using orthogonal frequency-division multiplexing (OFDM)signals. By accurately knowing a radio environment representation, basestation performance may be improved. For example, base stationapplications, such as user handover, radio resource management, linkadaption, filter and channel estimation, may use geographical locationspecific HiDi radio environment representations to improve managementand communication performance.

In particular, power angle delay profiles (PADPs) for particulargeographical locations in a cell of a cellular network may be obtainedand stored in a database that may be accessible by base stationapplications. Similarly, spatial-time (ST) and spatial-frequency (SF)correlations of channel impulse responses may be used to obtain locationspecific HiDi radio environment representation. Power values may beobtained for specific geographical locations by the correlations andstored in a database accessible by base station applications. HiDi radioenvironment representations capture both spatial domain and time domainchannel characteristics by taking advantage of complexness of the reallife channel.

In an embodiment, when using a SF correlation based representation of aradio environment, localization performance may have an aboveapproximate 90% detection accuracy with several antennas and frequencychannel samples.

It is understood that the present technology may be embodied in manydifferent forms and should not be construed as being limited to theembodiments set forth herein. Rather, these embodiments are provided sothat this disclosure will be thoroughly and completely understood.Indeed, the disclosure is intended to cover alternatives, modificationsand equivalents of these embodiments, which are included within thescope and spirit of the disclosure as defined by the appended claims.Furthermore, in the following detailed description, numerous specificdetails are set forth in order to provide a thorough understanding ofthe technology. However, it will be clear that the technology may bepracticed without such specific details.

FIG. 1 illustrates a system including a cellular network 100 having aplurality of cells 120-123 forming a wireless network according toembodiments of the present technology. FIG. 1 also illustrates anexpanded view of cell 120 having a base station 130 that communicateswith one or more UEs, such as UE 114, in cell 120. A base station 130may include antenna 111 coupled to computing device 112 in anembodiment. Antenna 111 may include a plurality of directional antennasor antenna elements and may be coupled to an antenna tower or otherphysical structure in embodiments. Antenna 111 may transmit and receivesignals, such as OFDM signals, to and from UEs in cell 120 in responseto electronic signals from and to computing device 112. In anembodiment, antenna 111 includes a multi-input and multi-output (MIMO)antenna. In embodiments, base station 130 includes one or moretransceivers coupled to antenna 111 to transmit and receive RF signalsto and from UE 114 in cell 120. Computing device 112 may beelectronically coupled to other antennas and/or other cells, such asantennas in cells 121-123, in alternate embodiments.

Cell 120 may cover a very different radio environment than one or morecells 121-123. For example, cell 120 may cover a large urban area withmany large and irregular spaced structures, such as buildings 113;while, one or more cells 121-123 may cover rural areas that may includea relatively flat topography with very few high structures. Because ofthe relatively complex radio environment of cell 120, signalstransmitted by UE 114 in cell 120 may reflect or form a multipath inarriving at antenna 111. For example, a signal transmitted by UE 114 ata particular geographical location may result in multiple signalsarriving at antenna 111 at different times and angles, or rays. A signaltransmitted from UE 114 may arrive at antenna 111 as at least twodifferent signals 115 and 116 with different angles of arrival andrelative delays. Signal 116 may arrive at antenna 111 as a reflected anddelayed signal from buildings 113.

According to embodiments of the present technology, computing device 112along with at least PADP 112 a and uplink estimation 112 b softwarecomponents, as described in detail herein, obtain a HiDi radioenvironment representation of cell 120. In an embodiment, computingdevice 112 executes uplink estimation 112 b to obtain and store PADPsand/or average power values in PADP 112 a. PADPs and/or average powervalues for UEs transmitting from particular geographical locations incell 120 may be stored in a database by uplink estimation 112 b. HiDiradio environment representations may also be obtained by spatial-timeand spatial-frequency correlations of channel impulse responses that mayor may not include a power value as described in detail herein. A storedHiDi radio environment representation of cell 120 may be accessed bybase station applications executed by computing device 112 in order toimprove performance of base station 130. For example, stored HiDi radiorepresentations for particular geographical locations in cell 120 may beaccessed and used by base station 130 to improve user handover, radioresource management, link adaption, filtering and channel estimation.

In embodiments, a UE 114 is also known as mobile station (MS). In anembodiment, UE 114 conforms to a SIMalliance, Device ImplementationGuide, June 2013 (SIMalliance) specification. In other embodiments, UE1114 does not conform to the SIMalliance specification.

In embodiments, base station 130 may be second generation (2G), thirdgeneration (3G), fourth generation (4G) and/or fifth generation (5G)base station. In embodiments, different types of cellular technologiesmay be used, such as Global System for Mobile Communications (GSM), codedivision multiple access (CDMA), Time division multiple access (TDMA)and Advanced Mobile Phone System (AMPS) (analog). In embodiments,different types of digital cellular technologies may be used, such as:GSM, General Packet Radio Service (GPRS), cdmaOne, CDMA2000,Evolution-Data Optimized (EV-DO), Enhanced Data Rates for GSM Evolution(EDGE), Universal Mobile Telecommunications System (UMTS), DigitalEnhanced Cordless Telecommunications (DECT), Digital AMPS (IS-136/TDMA),and Integrated Digital Enhanced Network (iDEN).

In embodiments, base station 130 may be an E-UTRAN Node B (eNodeB), NodeB and/or Base Transceiver Station (GBTS) BS. A GBTS may operate avariety of type's wireless technology, such as CDMA, GSM, WorldwideInteroperability for Microwave Access (WiMAX) or Wi-Fi. A GBTS mayinclude equipment for the encryption and decryption of communications,spectrum filtering equipment, antennas and transceivers. A GBTStypically has multiple transceivers that allow it to serve many of thecell's different frequencies and sectors.

FIG. 2 illustrates a PADP according to embodiments of the presenttechnology. A radio channel from a particular location in a cell that aUE may transmit to a base station is represented by a power value,vertical angle of arrival, horizontal angle of arrival and delay valueas illustrated by equation 201. FIG. 2 illustrates an orientation 200 ofan antenna 204 (or an array of antenna elements) with respect to areceived signal or ray 206 from UE 205 having a horizontal angle ofarrival Φ, a vertical angle of arrival of θ and delay value τ_(s). Adelay value may be relative to a first channel tap arrived at the basestation as illustrated by equation 202. In embodiments, transmit powerat different locations are normalized to the unit and τ₁=min τ_(s). Inan embodiment, antenna 204 and UE 205 correspond to antenna 111 and UE114 in FIG. 1.

FIG. 3A is a block diagram 300 that illustrates obtaining power angledelay profiles for particular geographical locations in a cell accordingto embodiments of the present technology. In an embodiment, a pluralityof received signals {Y} are input to angle domain (DOA) based channelestimation 301. In an embodiment, the plurality of signals {Y} areobtained from one or more antennas (or antenna elements) that receivesan OFDM signal from one or more UEs.

In embodiments, a plurality of received signals {Y} include a receivedsignal set {y_(mk)} over multiple time instances, where y_(mk) is areceived signal at the m-th antenna, k-th subcarrier. In an embodiment,DOA based channel estimation 301 outputs complex channel values for ahorizontal angle of arrival and vertical angle of arrival per ray in aplurality of rays received—{[H_(k)(θ_(s),Φ_(s)), θ_(s), Φ_(s)]| s=1, . .. N_(rays)}. In an embodiment, DOA based channel estimation 301 outputsan estimated channel state in the frequency domain.

In an embodiment, DOA based channel estimation 301 is calculated byusing an array signal processing method include one of N-point discretefourier transform (DFT) steering, minimum variance distortionlessresponse (MVDR) and multiple signal classification (MUSIC). In anembodiment, DOA based channel estimation 301 calculates a channelestimation before receiving signals.

Frequency-offset compensation 302 applies a frequency-offsetcompensation (or estimations) to the complex channel values input fromDOA based channel estimation 301. In an embodiment, a frequency-offsetcompensation is applied as illustrated in frequency-offset estimationequations 375 in FIG. 3C, and in particular a frequency-offsetestimation is applied using equation 380. Frequency-offset compensation302 outputs frequency compensated complex channel values{[H′_(k)(θ_(s),Φ_(s)), θ_(s), Φ_(s)]| s=1, . . . N_(rays)} to inversefast fourier transform (IFFT) 303.

IFFT 303 performs an inverse fast fourier transform on frequencycompensated complex channel values {[H′_(k)(θ_(s),Φ_(s)), θ_(s), Φ_(s)]|s=1, . . . N_(rays)} to output an estimated channel state in the timedomain {[h′_(k)(θ_(s),Φ_(s)), θ_(s), Φ_(s), τ′_(s)]| s=1, . . .N_(rays)} that is input to time-offset compensation 304.

Time-offset compensation 304 provides a time-offset compensation to anestimated channel state in the time domain {[h′_(k)(θ_(s),Φ_(s)), θ_(s),Φ_(s), τ′_(s)]| s=1, . . . N_(rays)}. In an embodiment, a time-offsetestimation is first applied as illustrated in time-offset estimationequations 350 in FIG. 3B, and in particular a time-offset estimation isapplied using equation 356. Time-offset compensation 304 outputstime-offset compensated channel values [h(θ_(s),Φ_(s)), θ_(s), Φ_(s),τ′_(s)−τ′₁]| s=1, . . . N_(rays)} to per ray power estimation 305.

Per ray power estimation 305 obtains an expected value or averagestiming compensated channel values in the time domain h(θ_(s),Φ_(s)) toobtain an average power value P_(s) for horizontal angle of arrival,vertical angle of arrival and time delay for the plurality of rays. Theaverage power value P_(s) and associated geographical location of thetransmitting UE may then be stored in a database that is accessible by abase station application.

FIG. 3B illustrates time-offset estimation equations 350 according toembodiments of the present technology. In embodiments, time-offsetcompensation 304, time-offset compensation 404 and time-frequency-offsetcompensation 502 shown in FIGS. 3A, 4A and 5A perform at least a portionof equations 350 illustrated in FIG. 3B. Similarly, in softwarecomponent embodiment's time-offset compensation 901 d and 902 d as wellas time-frequency-offset compensations 903 b perform at least a portionof the equations 350 of FIG. 3B.

Equation 351 illustrates an angle channel in the time domain where eachcomponent is frequency flat in an embodiment.

Equation 352 illustrates a frequency domain channel in an embodiment.

Equation 353 illustrates a frequency channel correlation in anembodiment.

Equation 354 illustrates a time-offset to obtain angular phase in anembodiment.

Equation 355 illustrates an average correlation on the estimatedfrequency domain channel in embodiment.

Equation 356 illustrates a time-offset estimation that may be used inembodiments.

FIG. 3C illustrates frequency-offset estimation equations 375 accordingto embodiments of the present technology. In embodiments,frequency-offset compensation 302, frequency-offset compensation 402 andtime-frequency-offset compensation 502 shown in FIGS. 3A, 4A and 5Aperform at least a portion of equations 375 illustrated in FIG. 3C.Similarly, in software component embodiment's frequency-offsetcompensation 901 b and 902 b as well as time-frequency-offsetcompensations 903 b perform at least a portion of the equations 375 inFIG. 3C.

Equation 376 illustrates a frequency domain channel with a frequencyoffset in an embodiment.

Equation 377 illustrates frequency domain channel correlation ofdifferent OFDM symbols and assumes that a channel does not change oftime (I′−I is small) in an embodiment.

Equation 378 illustrates a frequency-offset to obtain an angular phasein an embodiment.

Equation 379 illustrates an average correlation on the estimatedfrequency domain channel in an embodiment.

Equation 380 illustrates a frequency-offset estimation that may be usedin embodiments. A frequency-offset estimation is assumed to be the samefor different rays in an embodiment. In an embodiment, frequency-offsetestimations for different rays may be averaged to improve performance.When frequency-offset estimations are different over rays,per-ray-frequency-offset estimations may be performed similarly in anembodiment.

FIGS. 3D-E illustrates a relationship between power delay profiles andfrequency correlations according to embodiments of the presenttechnology. In particular, FIG. 3D illustrates power delay profiles forextended pedestrian A (EPA), Extended Vehicle A (EVA) and ExtendedTypical Urban (ETU) models. In each model, power values of signals arecharted at particular delay values.

FIG. 3E illustrates frequency domain correlation versus subcarrier stepor spacing in an OFDM signal for each model. In an embodiment,subcarrier spacing Δn_(f) is the same as subcarrier spacing Δk as seenin equation 590 of FIG. 5D. In comparing FIGS. 3D and 3E, frequencydomain correlation captures power delay profiles to a certain extent.

FIG. 4A is a block diagram 400 that illustrates obtaining spatial-timecorrelations for UEs transmitting at particular geographical locationsin a cell according to embodiments of the present technology. In anembodiment, a plurality of received signals are input to frequencydomain channel estimation 401. In embodiments, a plurality of receivedsignals {Y} include a received signal set {y_(mk)} over multiple timeinstances, where y_(mk) is a received signal at the m-th antenna, k-thsubcarrier as described similarly herein. In an embodiment, frequencydomain channel estimation 401 outputs an estimated channel state in thefrequency domain {H} to frequency-offset compensation 402 in response tothe received signals {Y}.

Frequency-offset compensation 402 provides a frequency-offset estimationto an estimated channel state in the frequency domain {H} and thencompensates the frequency-offset on the estimated channel. In anembodiment, a frequency-offset estimation is applied as illustrated infrequency-offset estimation equations 375 of FIG. 3C, and in particulara frequency-offset estimation is applied using equation 380.Frequency-offset compensation 402 outputs frequency-offset compensatedestimated channel state in the frequency domain {H′} to IFFT 403 inresponse to receiving an estimated channel state in the frequency domain{H}.

IFFT 403 performs an inverse fast fourier transform on a receivedfrequency-offset compensated estimated channel state in the frequencydomain {H′} to output an estimated channel state in the time domain {h′}that is then input to time-offset compensation 404.

Time-offset compensation 404 provides a time-offset estimation to anestimated channel state in the time domain {h′}. In an embodiment, atime-offset compensation is applied as illustrated in time-offsetestimation equations 350 of FIG. 3B, and in particular a time-offsetestimation is applied using equation 356. Time-offset compensation 404outputs a time-offset compensated channel state in the time domain {h}to spatial-temporal vectorization 902 e in response to receiving anestimated channel state in the time domain {h′}.

Spatial-temporal vectorization 406 outputs a vector {vec(h)} orvectorizes a received time-offset compensated channel state in the timedomain {h}. In an embodiment, vec(h)=[h₁₁, . . . , h_(1NTs), h₂₁, . . ., h_(2NTs), . . . , h_(mn), . . . , h_(M1), . . . h_(MNTs)]^(T) whereNTs (or N_(TS)) equals the number of time domain channel sampling pointsfor ST correlation estimations. In an embodiment, spatial-temporalvectorization 406 outputs a vector {vec(h)} to spatial-temporal channelimpulse response (CIR) correlation 407 in response to receiving atime-compensated channel state in the time domain {h′}.

Spatial-temporal CIR correlation 407 outputs a spatial-time correlationR_(ST) in response to receiving a vector {vec(h)}. In an embodiment, aspatial-time correlation R_(ST) may be estimated atR_(ST)=E{vec(h)*vech(h)^(H)}. In an embodiment, an average power valueestimation P for a UE transmitting from a particular geographicallocation may be obtained by an Eigen decompensation of the spatial-timecorrelation R_(ST) as illustrated in FIG. 4B.

FIG. 4B illustrates represents the relationship between PADPs andspatial-time correlations as well as radio environment representationswith spatial-time correlations of the channel state informationaccording to embodiments of the present technology. In particular, FIG.4B illustrates equation 450 that represents the relationship betweenspatial-time correlations R_(mn,n′n) (or R_(ST)) and PADPs. For example,equation portion 450 a represents data sampling of the system andequation portion 450 b represents the relation with PADPs. Equationportion 450 b illustrates that delay values relative to the first tapand angles are encapsulated in the final result shown as equationportion 450 c that includes a set of power values P_(s) (or P) for aparticular geographical location. In an embodiment, small scale fadingfor each ray component is independent. Eigen decomposition ofspatial-time correlation R_(ST) is illustrated by equation 451 with aradio environment representation in equation 452. A set of P values inequation 452 may be averaged to obtain average power value P for a UEtransmitting from particular geographical location.

FIG. 4C is a block diagram 400C that illustrates obtaining another typeof spatial-time correlations for UEs transmitting at particulargeographical locations in a cell according to embodiments of the presenttechnology. In an embodiment, a plurality of received signals are inputto frequency domain channel estimation 401C. In embodiments, a pluralityof received signals {Y} include a received signal set {y_(mk)} overmultiple time instances, where y_(mk) is a received signal at the m-thantenna, k-th subcarrier as described similarly herein. In anembodiment, frequency domain channel estimation 401C outputs anestimated channel state in the frequency domain {H} to frequency-offsetcompensation 402B in response to the received signals {Y}.

Frequency-offset compensation 402C provides a frequency-offsetestimation to an estimated channel state in the frequency domain {H} andthen compensates the frequency-offset on the estimated channel. In anembodiment, a frequency-offset estimation is applied as illustrated infrequency-offset estimation equations 375 of FIG. 3C, and in particulara frequency-offset estimation is applied using equation 380.Frequency-offset compensation 402C outputs frequency-offset compensatedestimated channel state in the frequency domain {H′} to IFFT 403C inresponse to receiving an estimated channel state in the frequency domain{H}.

IFFT 403C performs an inverse fast fourier transform on a receivedfrequency-offset compensated estimated channel state in the frequencydomain {H′} to output an estimated channel state in the time domain {h′}that is then input to time-offset compensation 404C.

Time-offset compensation 404C provides a time-offset estimation to anestimated channel state in the time domain {h′}. In an embodiment, atime-offset compensation is applied as illustrated in time-offsetestimation equations 350 of FIG. 3B, and in particular a time-offsetestimation is applied using equation 356. Time-offset compensation 404Coutputs a time-offset compensated channel state in the time domain {h}to convolutional process 405C.

In 415C, a pseudo pilot sequence is generated in time domain. The pseudopilot sequence can be predefined sequence. The symbols in the pseudopilot sequence can have equal power to have better performance.

In 405C, the convolutional signal between the time-offset compensatedchannel values in the time domain {h} from 404B and the pseudo pilotsequence from 415C is obtained, i.e., s=h⊗x_(p), where ⊗ denotes theconvolution.

Spatial-temporal vectorization 406C outputs a vector {vec(s)} orvectorizes a received convolutional signal in the time domain {s}. In anembodiment, vec(s)=[S₁₁ . . . S_(1INTs), . . . , S_(2NTs), . . . ,S_(mn), . . . S_(M1), . . . S_(MNTs)]^(T) where NTs (or N_(TS)) equalsthe number of time domain sampling points on the convolutional signalfor ST correlation estimations. In an embodiment, spatial-temporalvectorization 406C outputs a vector {vec(s)} to spatial-temporalconvolutional channel impulse response (CIR) correlation 407C inresponse to receiving a convolutional signal in the time domain {s}.

Spatial-temporal correlation 407C outputs a spatial-time correlationR_(ST) in response to receiving a vector {vec(s)}. In an embodiment, aspatial-time correlation R_(ST) may be estimated atR_(ST)=E{vec(s)*vech(s)^(H)}. In an embodiment, an average power valueestimation P for a UE transmitting from a particular geographicallocation may be obtained by an Eigen decompensation of the spatial-timecorrelation R_(ST) as illustrated in FIG. 4B.

FIG. 4D is a block diagram 400D that illustrates obtaining another typeof spatial-time correlations for UEs transmitting at particulargeographical locations in a cell according to embodiments of the presenttechnology. In an embodiment, a plurality of received signals are inputto frequency domain channel estimation 401D. In embodiments, a pluralityof received signals {Y} include a received signal set {y_(mk)} overmultiple time instances, where y_(mk) is a received signal at the m-thantenna, k-th subcarrier as described similarly herein. In anembodiment, frequency domain channel estimation 401D outputs anestimated channel state in the frequency domain {H} totime-frequency-offset compensation 402D in response to the receivedsignals {Y}.

Time-frequency-offset compensation 402D provides a time-offset and afrequency offset estimation to an estimated channel state in thefrequency domain {H} and then compensates the estimated time andfrequency offsets on the estimated channel. In an embodiment, atime-frequency-offset estimation is applied as illustrated intime-offset estimation equations 350 and frequency-offset estimationequations 375 shown in FIGS. 3B-C, and in particular, a time-offsetestimation and frequency-offset estimation is applied using equations356 and 380 in FIGS. 3B-C. Time-frequency-offset compensation 402Doutputs time-frequency-offset compensated estimated channel state in thefrequency domain {H′} to signal multiplication 419D, which signal isconverted according to S(f)=H′(f)X_(p)(f) in response to receiving anestimated channel state in the frequency domain {H}, and the calculationis then output to IFFT 403D.

In 415D, a pseudo pilot sequence is generated in time domain. The pseudopilot sequence X_(p) can be predefined sequence. The symbols in thepseudo pilot sequence can have equal power to have better performance.

In 417D, a fast fourier transform is performed on the pseudo pilotsequence X_(p) to output the fast fourier transform X_(p)(f).

In 419D, the multiplication signal S(f) is calculated between thetime-off compensated channel values in the time domain {h} from 402C andthe pseudo pilot sequence from 419D is obtained, i.e.,S(f)=H′(f)X_(p)(f), where the multiplication is piecewise multiplicationfor each element H′(f) and each element X_(p)(f) that are on the samefrequency or the same subcarrier.

IFFT 403D performs an inverse fast fourier transform on a receivedfrequency-offset compensated estimated channel state in the frequencydomain {H′} to output an estimated channel state in the time domain {h′}that is then input to spatial-temporal vectorization 406D.

Spatial-temporal vectorization 406D outputs a vector {vec(s)} orvectorizes a received convolutional signal in the time domain {s}. In anembodiment, vec(s)=[S₁₁ . . . S_(1INTs), . . . , S_(2NTs), . . . ,S_(mn), . . . S_(M1), . . . S_(MNTs)]^(T) where NTs (or N_(TS)) equalsthe number of time domain sampling points on the convolutional signalfor ST correlation estimations. In an embodiment, spatial-temporalvectorization 406D outputs a vector {vec(s)} to spatial-temporalconvolutional channel impulse response (CIR) correlation 407C inresponse to receiving a convolutional signal in the time domain {s}.

Spatial-temporal correlation 407D outputs a spatial-time correlationR_(ST) in response to receiving a vector {vec(s)}. In an embodiment, aspatial-time correlation R_(ST) may be estimated atR_(ST)=E{vec(s)*vech(s)^(H)}. In an embodiment, an average power valueestimation P for a UE transmitting from a particular geographicallocation may be obtained by an Eigen decompensation of the spatial-timecorrelation R_(ST) as illustrated in FIG. 4B.

FIGS. 4E-F illustrate frequency correlation using different subcarrierspacing according to embodiments of the present technology. Inparticular, FIGS. 4E-F illustrate a resource grid 475 having a pluralityof resource elements that represent received OFDM symbols from aplurality of subcarrier signals in an OFDM signal at an antenna from aUE, such as UE-1. In an embodiment, UE-1 correspond to UE 114 shown inFIG. 1 and UE-2 represents a different UE transmitting to antenna 111 incell 120. In an embodiment, resource grid 475 represents resourceelements from a single antenna (m=1) or antenna element, such as antenna111 shown in FIG. 1. In alternate embodiments, a resource grid mayrepresent a plurality of resource elements from a plurality of antennasor antenna elements.

For example, resource grid 475 includes a plurality of rectangles orresource elements, such as resource element 480 that may represent atime interval (time slot or OFDM symbol period) to sample or obtain asignal value from an OFDM signal (in some embodiments a resource elementmay not be used). In an embodiment, a plurality of resource elements maybe grouped in a block to form a resource block. Columns of resource grid475 may represent a plurality of resource elements at a particular timeinterval and rows may represent subcarrier signals that have frequenciesseparated by a OFDM subcarrier frequency space (or spacing). Forexample, the first row of resource grid 475 may represent 14 OFDMsymbols of a first subcarrier signal having a frequency in an OFDMsignal; while the second row represents 14 OFDM symbols of a secondsubcarrier signal in an OFDM signal having a frequency of f+Δf (orspacing). A particular subcarrier signal in a plurality of subcarriersignals may be identified with an index (or number) or subcarrier index.Similarly, a particular OFDM symbol in a plurality of OFDM symbols maybe identified by another index (or another number) or OFDM symbol index.

Resource elements may include OFDM symbols that may represent dataand/or control signals in embodiments. In other embodiments, resourceelements may not be used or used for reference signals. For example,resource elements 478 a-n include wide band sound reference signals(SRSs) and columns of resource elements 476 and 477 include demodulationreference signals (DMRSs or DM-RSs) in embodiments.

As described in detail herein, FIG. 4E illustrates obtaining frequencydomain CIR correlations for particular geographical locations by usingdifferent subcarrier signal spacing. In particular, equation 490describes obtain correlations using different subcarrier frequencyspacing (m-th antenna, k-th subcarrier) shown in FIG. 4E.

FIG. 5D and equation 590, as described below, also illustrates obtainingan average correlation based on different subcarrier spacing Δk.

FIG. 5A is a block diagram 500 that illustrates obtaining spatial-timecorrelations for UEs transmitting at particular geographical locationsin a cell according to embodiments of the present technology. In anembodiment, a plurality of received signals are input to frequencydomain channel estimation 501. In embodiments, a plurality of receivedsignals {Y} include a received signal set {y_(mk)} over multiple timeinstances, where y_(mk) is a received signal at the m-th antenna, k-thsubcarrier as described similarly herein. In an embodiment, frequencydomain channel estimation 501 outputs an estimated channel state in thefrequency domain {H} to time-frequency-offset compensation 502 inresponse to the received signals {Y}.

Time-frequency-offset compensation 502 provides a time andfrequency-offset estimation and compensates the estimated time andfrequency offsets to an estimated channel state in the frequency domain{H}. In an embodiment, a time-frequency-offset estimation is applied asillustrated in time-offset estimation equations 350 and frequency-offsetestimation equations 375 shown in FIGS. 3B-C. In embodiments, atime-offset estimation and frequency-offset estimation is applied usingequations 356 and 380 in FIGS. 3B-C. Time-frequency-offset compensation502 outputs time-frequency offset compensated estimated channel state inthe frequency domain {H′} to spatial-frequency vectorization 503.

Spatial-frequency vectorization 503 outputs a vector {vec(H)} orvectorizes a received time-frequency offset compensated channel state inthe frequency domain {H}. In an embodiment, vec(H)=[H′₁₁, . . . ,H′_(1NFs), H′₂₁, . . . , H′_(2NFs), . . . , H′_(mk), . . . , H_(M1), . .. , h_(MNFs)]^(T) where NFs (or N_(FS)) equals the number of frequencydomain channel sampling points for SF correlation estimations. In anembodiment, spatial-frequency vectorization 503 outputs a vector{vec(H)} to spatial-frequency CIR correlation 504 in response toreceiving a frequency-time-compensated channel state in the frequencydomain {H′}.

Spatial-frequency CIR correlation 504 outputs a spatial-frequencycorrelation R_(SF) in response to receiving a vector {vec(H)}. In anembodiment, a spatial-frequency correlation R_(SF) may be estimated atR_(SF)=E{vec(H) vech(H)^(H)}. In an embodiment, an average power valueestimation P for a UE transmitting from a particular geographicallocation may be obtained by an Eigen decompensation of thespatial-frequency correlation R_(SF) as illustrated in FIG. 5B.

FIG. 5B illustrates the relationship between the PADP andspatial-frequency correlations as well as radio environmentrepresentations with spatial-frequency correlations of the channel stateinformation according to embodiments of the present technology. Inparticular, FIG. 5B illustrates equation 550 that represent therelationship between spatial-frequency correlations R_(mk,m′k′) (orR_(SF)) and PADPs. For example, equation portion 550 a represents datasampling of the system and equation portion 550 b represents therelation with PADPs. Equation portion 550 b illustrates that delayvalues relative to the first tap and angles are encapsulated in thefinal result shown as equation portion 550 c that includes a set ofpower values P_(s) (or P) for a particular geographical location. Asshown in FIG. 5B, equation 550 c depends on Δk=k−k′ and not an arbitrarysubcarrier location in an embodiment. Eigen decomposition ofspatial-frequency correlation R_(SF) is illustrated by equation 551 witha radio environment representation in equation 552. A set of P values inequation 552 may be averaged to obtain average power value for aparticular geographical location.

FIG. 5C illustrates spatial-frequency correlation of signals receivedfrom different antennas according to embodiments of the presenttechnology. In particular, FIG. 5C illustrates spatial-frequency CIRcorrelations 575 between different received subcarrier signals of anOFDM signal at different resource elements or time intervals atdifferent antennas, such as antennas m=1, m=2 . . . m=M_(T). In anembodiment, spatial-frequency correlations are performed using aplurality of subcarriers signals received at a plurality antennas, suchas sound reference signal (SRS) signals in subcarriers signals atdifferent subcarrier spacing in an OFDM signal. In an embodiment, FIG.5C illustrates the spatial-frequency correlation performed byspatial-frequency correlation 504 shown in 5A and/or correlation 903 dshown in FIG. 10.

FIG. 5D illustrates frequency correlation using different subcarrierspacing for a particular antenna according to embodiments of the presenttechnology. 501 illustrate frequency correlation between subcarrierssignals having a spacing of 2 subcarriers for a particular antenna, suchas m=1. 592 illustrates frequency correlation between subcarrierssignals having a spacing of 4 subcarriers for a particular antenna, suchas m=1. 593 illustrates frequency correlation between subcarrier signalshaving a spacing of 6 subcarriers for a particular antenna, such as m=1.Equation 590 describes obtaining an average correlation by averaging thecorrelations over different subcarrier spacing for a particular antenna,such as m=1.

FIGS. 6, 7A-B and 8 are flowcharts that illustrate methods according toembodiments of the present technology. In embodiments, flowcharts inFIGS. 6, 7A-B and 8 are computer-implemented methods performed, at leastpartly, by hardware and software components illustrated in FIGS. 1 and9-10 and as described herein. In an embodiment, software components inFIG. 10, executed by one or more processors, such as processor 910 shownin FIG. 9, perform at least a portion of the methods.

FIG. 6 is a flowchart that illustrates a method 600 for obtaining PADPsaccording to embodiments of the present technology. In FIG. 6 at 601, aplurality of received signals transmitted by a user equipment from aplurality of antenna elements in a cellular network is obtained. In anembodiment, receive 904 executed by processor 910 performs at least aportion of this function as described herein and illustrated in FIG. 9.

At 602 a plurality of complex channel values in an angle domain arecalculated for a horizontal arrival angle and a vertical arrival angleper a received ray in a plurality of received rays in response to theplurality of received signals. In an embodiment, UL estimation 901executed by processor 910 performs at least a portion of this functionas described herein and illustrated in FIGS. 9-10. In an embodiment, ULestimation 901 executed by processor 910 performs at least portions ofthe following functions described in method 600.

At 603 a frequency-offset estimation for the plurality of complexchannel values is calculated. In an embodiment, frequency-offsetcompensation 901 b of UL estimation 901 as shown in FIG. 10 is used.

At 604 the frequency-offset estimation for the plurality of complexchannel values is applied to obtain a plurality of offset complexchannel values for the horizontal arrival angle and the vertical arrivalangle per the received ray. In an embodiment, frequency-offsetcompensation 901 b of UL estimation 901 is used.

At 605 the plurality of offset complex channel values for the horizontalarrival angle and the vertical arrival angle per the received ray aretransformed to a plurality of channel values in a time domain for thehorizontal arrival angle and vertical arrival angle with a first timedelay per the received ray. In an embodiment, IFFT 901 c of ULestimation 901 is used.

At 606 a time-offset estimation is calculated for the plurality ofchannel values in the time domain. In an embodiment, time-offsetcompensation 901 d of UL estimation 901 is used.

At 607 the timing-offset estimation is applied to the plurality ofchannel values in the time domain to obtain a plurality of channelvalues for the horizontal arrival angle and vertical arrival angle witha second time delay per the received ray. In an embodiment, time-offsetcompensation 901 d of UL estimation 901 is used.

At 608 an expected value of the plurality of channel values iscalculated to obtain a power value for the horizontal arrival angle andvertical arrival angle having a second time delay per the received ray.In an embodiment, expected 901 e of UL estimation 901 is used.

FIGS. 7A-B is a flowchart that illustrates a method 700 for obtainingspatial-temporal correlation according to embodiments of the presenttechnology. In FIG. 7A at 701 a plurality of subcarrier signals in anOFDM signal is received from the plurality of antennas at a plurality oftime intervals that are transmitted by a user equipment. In anembodiment, receive 904 executed by processor 910 performs at least aportion of this function as described herein and illustrated in FIG. 9.

At 702 a channel in a frequency domain is calculated in response to theplurality of subcarrier signals. In an embodiment, spatial-timecorrelation 902 executed by processor 910 performs at least a portion ofthis function as described herein and illustrated in FIGS. 9-10. In anembodiment, spatial-time correlation 902 executed by processor 910performs at least portions of the following functions described inmethod 700.

At 703 a frequency-offset estimation is calculated. In an embodiment,frequency-offset compensation 902 b of spatial-time correlation 902 asshown in FIG. 10 is used.

At 704 the frequency-offset estimation is applied to the channel in thefrequency domain to obtain a frequency-offset compensated channel in thefrequency domain. In an embodiment, frequency-offset compensation 902 bof spatial-time correlation 902 is used.

At 705 the frequency-offset compensated channel in the frequency domainis transformed to a channel in a time domain. In an embodiment, IFFT 902c of spatial-time correlation 902 is used.

At 706 a time-offset estimation is calculated. In an embodiment,time-offset compensation 902 d of spatial-time correlation 902 is used.

At 707 the time-offset estimation is applied to the channel in the timedomain to obtain a time-offset compensated channel in the time domain.In an embodiment, time-offset compensation 902 d of spatial-timecorrelation 902 is used.

At 708 the time-offset compensated channel in the time domain isvectorized to obtain a vector of the time-offset compensated channel inthe time domain. In an embodiment, spatial-temporal vectorization 902 eof spatial-time correlation 902 is used.

At 709 the vector is correlated to obtain a spatial-time correlation. Inan embodiment, spatial-time channel impulse response correlation 902 fis used.

At 710 in FIG. 7B, an Eigen decomposition from the spatial-timecorrelation is performed. In an embodiment, spatial-time channel impulseresponse correlation 902 f is used. In an embodiment, correlating thevector to obtain a spatial-time correlation includes correlating thevector to obtain an Eigen decomposition of the spatial time correlationwith or without an average power value.

At 711 a plurality of power values obtained from the spatial-timecorrelation are averaged to obtain an average power value. In anembodiment, a spatial-time correlation or an Eigen decomposition of thespatial-time correlation is obtained without obtaining or averagingpower values. In an embodiment, spatial-time channel impulse responsecorrelation 902 f is used.

At 712 a geographical location for the user equipment is obtained. In anembodiment, receive 904 executed by processor 910 performs at least aportion of this function as described herein and illustrated in FIG. 9.

At 713 the spatial-time correlation or Eigen decomposition of thespatial-time correlation, with or without an average power valueassociated with the geographical location is stored in a database. In anembodiment, receive 904 executed by processor 910 stores spatial-timecorrelations with or without power values associated geographicallocations in a spatial-time correlation database 906 in memory 930.

At 714 the spatial-time correlation or an Eigen decomposition of thespatial-time correlation with or without an average power valueassociated with the geographical location is retrieved. In anembodiment, base station applications stored in memory 930 executed byprocessor 910 performs at least a portion of this function. In anembodiment, a spatial-time correlation with or without an average powervalue and associated geographical location stored in spatial-timecorrelation database 907 may be accessed by base station applications906. In embodiments, spatial-time correlations and spatial-frequencycorrelations with or without an average power value are considered radiochannel representations for particular locations in a cell when thereare not sufficient antennas or antenna elements to obtain accuratePADPs. [Comment: spatial-time correlation with (or without) averagepower is considered as the radio channel representation for a location.Why the power value is given a special focus here? Also spatial-timecorrelation (or Eigen decomposition of spatial time correlation) with orwithout average power is a channel representation in parallel of PADP orindependent PADP. We do not need PADP to form channel representationwhen we have spatial-time correlation. Actually we consider spatial-timecorrelation and later spatial-frequency correlation as the channelrepresentation because for current systems or even in 5G we may not beable to get good PADP information as number of antennas is not largeenough.]

At 715 the spatial-time correlation or an Eigen decomposition of thespatial-time correlation with or without an average power value is usedin a base station application. In an embodiment, base stationapplications 906 executed by processor 910 performs at least a portionof this function. In embodiments, a base station application in basestation applications 906 includes, but is not limited to, functions toperform one of a user handover, radio resource management, linkadaptation, filter and channel estimation for a base stationcommunicating with user equipment in a cellular network.

FIG. 8 is a flowchart that illustrates a method 800 for obtaining aspatial-frequency correlation according to embodiments of the presenttechnology. In FIG. 8 at 801 a plurality of subcarrier signals in anOFDM signal is received at a plurality time intervals from a pluralityof antennas at a base station in a cellular network in which the signalsare transmitted by a user equipment. In an embodiment, receive 904executed by processor 910 performs at least a portion of this functionas described herein and illustrated in FIG. 9.

At 802 a channel in a frequency domain is calculated in response to theplurality of subcarrier signals. In an embodiment, spatial-frequencycorrelation 903 executed by processor 910 performs at least a portion ofthis function as described herein and illustrated in FIGS. 9-10. In anembodiment, spatial-frequency correlation 903 executed by processor 910performs at least portions of the following functions described inmethod 800.

At 803 a frequency-offset estimation and a time-offset estimation iscalculated. In an embodiment, timing-frequency-offset compensations 903b of spatial-frequency correlation 903 as shown in FIG. 10 is used.

At 804 the frequency-offset estimation and the time-offset estimation isapplied to the channel in the frequency domain to obtain atiming-frequency-offset compensated channel in the frequency domain. Inan embodiment, timing-frequency-offset compensations 903 b ofspatial-frequency correlation 903 is used.

At 805 the timing-frequency-offset compensated channel in a time domainis vectorized to obtain a vector of the timing-frequency-offsetcompensated channel in the time domain. In an embodiment, spatialfrequency vectorization 903 c of spatial-frequency correlation 903 isused.

At 806 the vector is correlated to obtain a spatial-frequencycorrelation with or without an average power value. In an embodiment, anEigen decomposition of the spatial-frequency correlation with or withoutan average power value is obtained. In an embodiment, correlation 903 dof spatial-frequency correlation 903 is used.

FIG. 9 illustrates a hardware architecture 900 for a computing device990 that obtains a high dimensional (HiDi) radio environmentrepresentation. In an embodiment, computing device 990 is included in abase station having an antenna that communicates with user equipment ina cellular network. In embodiments, computing device 990 obtains andstores PADPs for particular geographical locations in a cellularnetwork, such as cellular network 100 shown in FIG. 1. In an embodiment,computing device 990 obtains and stores spatial-time and/or spatialfrequency correlations with or without average power values, asdescribed herein, for particular geographical locations in the cellularnetwork, such as for a particular cell. The PADPs may be stored in adatabase, such as PADP database 905 stored in memory 930. Similarly,spatial-time correlations with or without average power values may bestored in database 907 and frequency-time correlations with or withoutaverage power values may be stored in database 908. In an embodiment,base station applications 906 may access values in PADP database 905,spatial-time correlation database 905 and/or spatial-frequency database908 to improve the management and/or performance of communication withuser equipment.

Computing device 990 may include a processor 910, memories 920-930, auser interface 960 and network interface 950 coupled by a interconnect970. Interconnect 970 may include a bus for transferring signals havingone or more type of architectures, such as a memory bus, memorycontroller, a peripheral bus or the like.

Computing device 990 may be implemented in various embodiments.Computing devices may utilize all of the hardware and softwarecomponents shown, or a subset of the components in embodiments. Levelsof integration may vary depending on an embodiment. For example, memory920 may comprise many more memories. Furthermore, a computing device 990may contain multiple instances of a component, such as multipleprocessors (cores), memories, databases, transmitters, receivers, etc.Computing device 990 may comprise a processor equipped with one or moreinput/output devices, such as network interfaces, storage interfaces,and the like.

In an embodiment, computing device 990 may be a mainframe computer thataccesses a large amount of data related to a cellular network stored ina database. In an alternate embodiment, computing device 990 may beembodied as different type of computing device. In an embodiment, typesof computing devices include but are not limited to, tablet, netbook,laptop, desktop, embedded, server and/or super (computer).

Memory 920 stores uplink (UL) estimation 901, spatial-time correlation902, spatial-frequency (SF) correlation 903 and receive 904 thatincludes computer instructions embodied in respective computer programs.In embodiments, other computer programs such as an operating systemhaving a scheduler, application(s) are stored in memory 920.

Memory 930 stores PADP database 905, spatial-time correlation database907, spatial-frequency database 908 and base station applications 906that similarly includes computer instructions embodied in respectivecomputer programs. In an embodiment, PADP database 905 includes PADPs orpower values, for horizontal arrival angles and vertical arrival anglesat time delays per ray in a plurality of rays received by an antenna forparticular geographical locations as described herein and shown in FIGS.2 and 3A. Similarly, spatial-time correlation database 907 andspatial-frequency database 908 that include correlations with or withoutaverage power values for particular geographical locations, such as GPScoordinates, in a cellular network as calculated by spatial-temporal andfrequency-temporal correlations as described herein (such as illustratedin FIGS. 4A and 5A.

In an embodiment, base station applications 906 may include base stationapplications that may aid in managing a base station as well asimproving communication with user equipment in a cellular network of thebase station. In an embodiment, base station applications 906 mayinclude, but are not limited to, a user handover, radio resourcemanagement, link adaptation, filter and channel estimation applications.

In an embodiment, processor 910 may include one or more types ofelectronic processors having one or more cores. In an embodiment,processor 910 is an integrated circuit processor that executes (orreads) computer instructions that may be included in code and/orcomputer programs stored on a non-transitory memory to provide at leastsome of the functions described herein. In an embodiment, processor 910is a multi-core processor capable of executing multiple threads. In anembodiment, processor 910 is a digital signal processor, basebandcircuit, field programmable gate array, digital logic circuit and/orequivalent.

A thread of execution (thread or hyper thread) is a sequence of computerinstructions that can be managed independently in one embodiment. Ascheduler, which may be included in an operating system, may also managea thread. A thread may be a component of a process, and multiple threadscan exist within one process, executing concurrently (one startingbefore others finish) and sharing resources such as memory, whiledifferent processes do not share these resources. In an embodiment, thethreads of a process share its instructions (executable code) and itscontext (the values of the process's variables at any particular time).

In a single core processor, multithreading is generally implemented bytime slicing (as in multitasking), and the single core processorswitches between threads. This context switching generally happens oftenenough that users perceive the threads or tasks as running at the sametime. In a multiprocessor or multi-core processor, multiple threads canbe executed in parallel (at the same instant), with every processor orcore executing a separate thread at least partially concurrently orsimultaneously.

Memories 920 and 930 may comprise any type of system memory such asstatic random access memory (SRAM), dynamic random access memory (DRAM),synchronous DRAM (SDRAM), read-only memory (ROM), a combination thereof,or the like. In an embodiment, a memory 920 may include ROM for use atboot-up, and DRAM for program and data storage for use while executingcomputer instructions. In embodiments, memories 920 and 930 arenon-transitory or non-volatile integrated circuit memory storage.

Further, memories 920 and 930 may comprise any type of memory storagedevice configured to store data, store computer programs includinginstructions, and store other information and to make the data, computerprograms, and other information accessible via interconnect 970.Memories 920 and 930 may comprise, for example, one or more of a solidstate drive, hard disk drive, magnetic disk drive, optical disk drive,or the like.

Computing device 990 also includes one or more network interfaces 950,which may comprise wired links, such as an Ethernet cable or the like,and/or wireless links to access network 980. A network interface 950allows computing device 990 to communicate with remote computing devicesand/or other cellular networks. For example, a network interface 950 mayprovide wireless communication via one or more transmitters/transmitantennas and one or more receivers/receive antennas.

Computing device 990 communicates or transfers information by way ofnetwork 980. In an embodiment, network 980 include a plurality of basestations in a cellular network or geographical regions and associatedelectronic interconnections. In an embodiment, network 980 may be wiredor wireless, singly or in combination. In an embodiment, network 980 maybe the Internet, a wide area network (WAN) or a local area network(LAN), singly or in combination.

In an embodiment, network 980 may include a High Speed Packet Access(HSPA) network, or other suitable wireless systems, such as for exampleWireless Local Area Network (WLAN) or Wi-Fi (Institute of Electrical andElectronics Engineers' (IEEE) 802.11x). In an embodiment, computingdevice 990 uses one or more protocols to transfer information orpackets, such as Transmission Control Protocol/Internet Protocol(TCP/IP) packets.

In embodiments, computing device 990 includes input/output (I/O)computer instructions as well as hardware components, such as I/Ocircuits to receive and output information from and to other computingdevices and/or BSs, via network 980. In an embodiment, an I/O circuitmay include at least a transmitter and receiver circuit.

In embodiments, functions described herein are distributed to other ormore computing devices. In embodiments, computing device 990 may act asa server that provides a service while one or more UE, computing devicesand/or associated base stations may act as a client. In an embodiment,computing device 990 and another computing device may act as peers in apeer-to-peer (P2P) relationship.

User interface 960 may include computer instructions as well as hardwarecomponents in embodiments. A user interface 960 may include inputdevices such as a touchscreen, microphone, camera, keyboard, mouse,pointing device and/or position sensors. Similarly, a user interface 960may include output devices, such as a display, vibrator and/or speaker,to output images, characters, vibrations, speech and/or video as anoutput. A user interface 960 may also include a natural user interfacewhere a user may speak, touch or gesture to provide input.

FIG. 10 illustrates a software architecture 1000 according toembodiments of the present technology. Software architecture 1000illustrates software components having computer instructions to obtain aHiDi radio environment representation. In embodiments, softwarecomponents illustrated in software architecture 1000 are stored inmemory 920 of FIG. 9. In embodiments, software components illustrated inFIGS. 9 and 10 may be embodied as a computer program, object, function,subroutine, method, software instance, script, code fragment, stored inan electronic file, singly or in combination. In order to clearlydescribe the present technology, software components shown in FIG. 10are described as individual software components. In embodiments, thesoftware components illustrated in FIG. 10, singly or in combination,may be stored (in single or distributed computer-readable storagemedium(s)) and/or executed by a single or distributed computing device(processor or multi-core processor) architecture. Functions performed bythe various software components described herein are exemplary. In otherembodiments, software components identified herein may perform more orless functions. In embodiments, software components may be combined orfurther separated.

In embodiments, software architecture 1000 includes UL estimation 901,ST correlation 902 and SF correlation 903.

In embodiments, UL estimation 901 includes angle domain (DOA) basedchannel estimation 901 a, frequency-offset compensation 901 b, inversefast fourier transform (IFFT) 901 c, time-offset compensation 901 d andexpect 901 e.

DOA based channel estimation 901 a is responsible for, among otherfunctions, outputting complex channel values in response to receiving aplurality of OFDM signals from an antenna. In embodiments, a pluralityof received signals {Y} include a received signal set {y_(mk)} overmultiple time instances, where y_(mk) is a received signal at the m-thantenna, k-th subcarrier. In an embodiment, DOA based channel estimation901 a outputs complex channel values for a horizontal angle of arrivaland vertical angle of arrival per ray in a plurality of raysreceived—{[H_(k)(θ_(s),Φ_(s)), θ_(s), Φ_(s)]| s=1, . . . N_(rays)}. Inan embodiment, DOA based channel estimation 901 a outputs an estimatedchannel state in the frequency domain.

In an embodiment, DOA based channel estimation 901 a is calculated byusing an array signal processing method include one of N-point DFTsteering, MVDR and MUSIC. In an embodiment, DOA based channel estimation901 a calculates a channel estimation before receiving signals.

Frequency-offset compensation 901 b is responsible for, among otherfunctions, providing a frequency-offset compensation to the complexchannel values output from DOA based channel estimation 901 a. In anembodiment, a frequency-offset compensation is applied as illustrated infrequency-offset equations 375 of FIG. 3C, and in particular afrequency-offset estimation is applied using equation 380.Frequency-offset compensation 901 b outputs frequency compensatedcomplex channel values {[H′_(k)(θ_(s),Φ_(s)), θ_(s), Φ_(s)]| s=1, . . .N_(rays)} to IFFT 901 c.

IFFT 901 c is responsible for, among other functions, performing aninverse fast fourier transform on frequency compensated complex channelvalues {[H′_(k)(θ_(s),Φ_(s)), θ_(s), Φ_(s)]| s=1, . . . N_(rays)} tooutput an estimated channel state in the time domain{[h′_(k)(θ_(s),Φ_(s)), θ_(s), Φ_(s), τ′_(s)]| s=1, . . . N_(rays)} thatis input to time-offset compensation 901 d.

Time-offset compensation 901 d is responsible for, among otherfunctions, providing a time-offset compensation to an estimated channelstate in the time domain {[h′_(k)(θ_(s),Φ_(s)), θ_(s), Φ_(s), τ′_(s)]|s=1, . . . N_(rays)}. In an embodiment, a time-offset compensation isapplied as illustrated in time-offset compensation estimation equations350 of FIG. 3B, and in particular a time-offset estimation is appliedusing equation 356. Time-offset compensation 901 d outputs timecompensated channel values {[h(θ_(s),Φ_(s)), θ_(s), Φ_(s), τ′_(s)−τ′₁]|s=1, . . . N_(rays)} with a time compensation to expected 901 e.

Expected 901 e is responsible for, among other functions, providing anexpected value or average of time compensated channel values in the timedomain h(θ_(s),Φ_(s)) to obtain an average power value P_(s) for ahorizontal angle of arrival, vertical angle of arrival and time delayper ray in a plurality of rays received.

In embodiments, ST correlation 902 includes frequency domain channelestimation 902 a, frequency-offset compensation 902 b, IFFT 902 c,time-offset compensation 902 d, spatial-temporal vectorization 902 e andspatial-temporal channel impulse response correlation 902 f.

Frequency domain channel estimation 902 a is responsible for, amongother functions, obtaining a frequency domain channel estimation. In anembodiment, Frequency domain channel estimation 902 a operates similarto DOA based channel estimation 901 a. In embodiments, a plurality ofreceived signals {Y} include a received signal set {y_(mk)} overmultiple time instances, where y_(mk) is a received signal at the m-thantenna, k-th subcarrier. In an embodiment, frequency domain channelestimation 902 a outputs an estimated channel state in the frequencydomain {H}.

Frequency-offset compensation 902 b is responsible for, among otherfunctions, providing a frequency-offset compensation to an estimatedchannel state in the frequency domain {H}. In an embodiment, afrequency-offset compensation is applied as illustrated in frequencyoffset equations 375 of FIG. 3C, and in particular a frequency-offsetestimation is applied using equation 380. Frequency-offset compensation902 b outputs frequency compensated estimated channel state in thefrequency domain {H′} to IFFT 902 c

IFFT 902 c is responsible for, among other functions, performing aninverse fast fourier transform on a frequency compensated estimatedchannel state in the frequency domain {H′} to output an estimatedchannel state in the time domain {h′} that is input to time-offsetcompensation 902 d.

Time-offset compensation 902 d is responsible for, among otherfunctions, providing a time-offset compensation to an estimated channelstate in the time domain {h′}. In an embodiment, a time-offsetcompensation is applied as illustrated in time-offset compensationestimation equations 350 of FIG. 3B, and in particular a time-offsetestimation is applied using equation 356. Timing-offset compensation 902d outputs estimated channel state in the time domain {h} tospatial-temporal vectorization 902 e.

Spatial-temporal vectorization 902 e is responsible for, among otherfunctions, forming a {vec(h)} or vectorizing the estimated channel statein the time domain {h}.

Spatial-temporal channel impulse response (CIR) correlation 902 f isresponsible for, among other functions, obtaining a spatial-timecorrelation R_(ST) from vector {vec(h)}. In an embodiment, an averagepower value P may be obtained by an Eigen decompensation of thespatial-time correlation R_(ST).

In embodiments, SF correlation 903 includes frequency domain channelestimation 903 a, time-frequency-offset compensations 903 b, spatialfrequency vectorization 903 c and correlation 903 d.

Frequency domain channel estimation 903 a is responsible for, amongother functions, obtaining a frequency domain channel estimation {H} inresponse to a plurality of received signals {Y}. In an embodiment,frequency domain channel estimation 903 a functions similarly tofrequency domain channel estimation 902 a described herein.

Time-frequency-offset compensations 903 b is responsible for, amongother functions, providing time and frequency offset compensations to afrequency domain channel estimation to output a time and frequencycompensated frequency domain channel estimation {H′}. In an embodiment,time-frequency-offset compensations 903 b functions similarly tofrequency-offset compensation 902 b and time-offset compensation 902 das described herein.

Spatial-frequency vectorization 903 c is responsible for, among otherfunctions, forming a {vec(H)} or vectorizing the time and frequencycompensated estimated channel state {H′}. In an embodiment,spatial-frequency vectorization 903 c functions similarly tospatial-temporal vectorization 902 e as described herein.

Correlation 903 d is responsible for, among other functions, isresponsible for, obtaining a spatial-frequency correlation R_(SF) fromvector {vec(H)}. In an embodiment, an average power value P may beobtained by an Eigen decompensation of the spatial-time correlationR_(SF). In an embodiment, correlation 903 d functions similarly tospatial-temporal CIR correlation 902 f as described herein.

Receive 904 is responsible for in embodiments, among other functions,obtaining a plurality of received signal values from an antenna or aplurality of antenna elements at a base station. Receive 904 is alsoresponsible for obtaining a geographical location, such as globalposition system (GPS) coordinates or other indication of location, for aUE transmitting to a base station in an embodiment. Receive 904 isresponsible for storing and/or retrieving PADPs and/or spatial-time andspatial-frequency correlations with or without average power valuesassociated with respective geographical locations in a cell in anembodiment. In still other embodiments, receive 904 may be responsiblefor averaging, such as average power values in a set of power values.

Advantages of the present technology may include, but are not limitedto, having a HiDi radio environment representation that has relativelybetter uniqueness for particular locations as compared to a typicalradio representation. Localization may be accomplished with a HiDi radioenvironment representation, particularly when a UE is at a locationwhere no line-of-sight (LoS) channel path exists.

The present HiDi radio environment representation technology may provideimproved performance, such as improved channel estimation, formulti-antenna systems as compared to one-dimensional radio environmentrepresentations. With a HiDi radio environment representation,appropriate channel estimation filters may be applied to improve channelestimation performance after a particular geographical location has beendetected.

Interference from neighboring base stations may be estimated for aparticular UE communicating with a multi-antenna multi-input andmulti-output (MIMO) base station with linear precoding; consequently,link adaption may be improved in an embodiment.

A cellular network may detect a location of a UE and retrieve channelcharacteristics for the UE from different cells even when the UE is notassociated with the cell. A serving base station may then prepare ahandover in advance for the UE to the appropriate cell.

Further, the present HiDi radio environment representation technologymay enable accurate signal-to-interference-plus-noise ratio (SINR)estimations for multi-user (MU) MIMO cellular networks with channelcovariance of the in cell UE's when performing MU-MIMO pairing andscheduling. This may achieve better resource management, particularlyfor large-scale MIMO cellular networks, in an embodiment.

ST and SF correlation based HiDi radio environment representations maywork well for cellular networks with a limited number of antennas, suchas current long-term evolution (LTE) systems.

With spatial channel statistical information encapsulated, HiDi radioenvironment representations may facilitate multi-cell MIMO coordinationand cellular network optimization in an embodiment.

The flowcharts and block diagrams in the figures illustrate thearchitecture, functionality, and operation of possible implementationsof a device, apparatus, system, computer-readable medium and methodaccording to various aspects of the present disclosure. In this regard,each block (or arrow) in the flowcharts or block diagrams may representoperations of a system component, software component or hardwarecomponent for implementing the specified logical function(s). It shouldalso be noted that, in some alternative implementations, the functionsnoted in the block may occur out of the order noted in the figures. Forexample, two blocks (or arrows) shown in succession may, in fact, beexecuted substantially concurrently, or the blocks (or arrows) maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block (or arrow)of the block diagrams and/or flowchart illustration, and combinations ofblocks (or arrows) in the block diagram and/or flowchart illustration,can be implemented by special purpose hardware-based systems thatperform the specified functions or acts, or combinations of specialpurpose hardware and computer instructions.

It will be understood that each block (or arrow) of the flowchartillustrations and/or block diagrams, and combinations of blocks (orarrows) in the flowchart illustrations and/or block diagrams, may beimplemented by non-transitory computer instructions. These computerinstructions may be provided to and executed (or read) by a processor ofa general purpose computer (or computing device), special purposecomputer, or other programmable data processing apparatus to produce amachine, such that the instructions executed via the processor, create amechanism for implementing the functions/acts specified in theflowcharts and/or block diagrams.

As described herein, aspects of the present disclosure may take the formof at least a system, a device having one or more processors executinginstructions stored in non-transitory memory, a computer-implementedmethod, and/or a non-transitory computer-readable storage medium storingcomputer instructions.

Non-transitory computer-readable media includes all types ofcomputer-readable media, including magnetic storage media, opticalstorage media, and solid state storage media and specifically excludessignals. It should be understood that software including computerinstructions can be installed in and sold with a computing device havingcomputer-readable storage media. Alternatively, software can be obtainedand loaded into a computing device, including obtaining the software viaa disc medium or from any manner of network or distribution system,including, for example, from a server owned by a software creator orfrom a server not owned but used by the software creator. The softwarecan be stored on a server for distribution over the Internet, forexample.

More specific examples of the computer-readable medium include thefollowing: a portable computer diskette, a hard disk, a random accessmemory (RAM), ROM, an erasable programmable read-only memory (EPROM orFlash memory), an appropriate optical fiber with a repeater, a portablecompact disc read-only memory (CD-ROM), an optical storage device, amagnetic storage device, or any suitable combination thereof.

Non-transitory computer instructions used in embodiments of the presenttechnology may be written in any combination of one or more programminglanguages. The programming languages may include an object orientedprogramming language such as Java, Scala, Smalltalk, Eiffel, JADE,Emerald, C++, CII, VB.NET, Python, R or the like, conventionalprocedural programming languages, such as the “c” programming language,Visual Basic, Fortran 2003, Perl, COBOL 2002, PHP, ABAP, dynamicprogramming languages such as Python, Ruby and Groovy, or otherprogramming languages. The computer instructions may be executedentirely on the user's computer (or computing device), partly on theuser's computer, as a stand-alone software package, partly on the user'scomputer and partly on a remote computer, or entirely on the remotecomputer or server. In the latter scenario, the remote computer may beconnected to the user's computer through any type of network, or theconnection may be made to an external computer (for example, through theInternet using an Internet Service Provider) or in a cloud computingenvironment or offered as a service such as a Software as a Service(SaaS).

Additional embodiments are illustrated herein by the following clauses.

Clause 1. A device comprises a non-transitory memory storinginstructions and one or more processors in communication with thenon-transitory memory. The one or more processors execute theinstructions to obtain a plurality of received signals transmitted by auser equipment from a plurality of antenna elements in a cellularnetwork. A plurality of complex channel values are calculated in anangle domain for a horizontal arrival angle and a vertical arrival angleper a received ray in a plurality of received rays in response to theplurality of received signals. A frequency-offset estimation iscalculated for the plurality of complex channel values. Thefrequency-offset estimation is applied to the plurality of complexchannel values to obtain a plurality of offset complex channel valuesfor the horizontal arrival angle and the vertical arrival angle per thereceived ray. The plurality of offset complex channel values for thehorizontal arrival angle and the vertical arrival angle per the receivedray are transformed to a plurality of channel values in a time domainfor the horizontal arrival angle and vertical arrival angle with a firsttime delay per the received ray. A time-offset estimation is calculatedfor the plurality of channel values in the time domain. The time-offsetestimation for the plurality of channel values in the time domain isapplied to obtain a plurality of channel values for the horizontalarrival angle and vertical arrival angle with a second time delay perthe received ray. An expected value of the plurality of channel valuesis calculated to obtain a power value for the horizontal arrival angleand vertical arrival angle having a second time delay per the receivedray.

Clause 2. The device of clause 1, wherein obtain the plurality ofreceived signals include obtain a plurality of orthogonalfrequency-division multiplexing (OFDM) signals and the plurality ofantenna elements are included in a multiple-input and multiple-output(MIMO) antenna.

Clause 3. The device of any one of clauses 1-2, wherein the plurality ofOFDM signals include a plurality of sounding reference signals in aplurality of subcarrier signals of a resource block transmitted by theuser equipment.

Clause 4. The device of any one of clauses 1-3, wherein calculate aplurality of complex channel values in the angle domain includes: obtainan angle domain based channel estimation and providing the plurality ofreceived signals to the angle domain based channel to obtain theplurality of complex channel values.

Clause 5. The device of any one of clauses 1-4, wherein obtain the angledomain based channel estimation includes using an array signalprocessing that includes one of N-point discrete fourier transform (DFT)steering, minimum variance distortionless response (MVDR) and multiplesignal classification (MUSIC).

Clause 6. The device of any one of the clauses 1-5, wherein the one ormore processors execute the instructions to: obtain a geographicallocation for the user equipment and store the power value, thehorizontal arrival angle, vertical arrival angle and a second time delayper the received ray for the geographical location in anothernon-transitory memory.

Clause 7. The device of any one of the clauses 1-6, wherein the deviceis included in a base station having the plurality of antenna elementsfor communicating with the user equipment in the cellular network,wherein the one or more processors execute the instructions to: retrievethe power value, the horizontal arrival angle, vertical arrival angleand a second time delay per the received ray for the geographicallocation to use in one of user handover, radio resource management, linkadaptation and channel estimation in the base station.

Clause 8. A computer-implemented method for a base station having aplurality of antennas to communicate with a user equipment in a cellularnetwork comprises the steps of: receiving a plurality of subcarriersignals in an orthogonal frequency-division multiplexing (OFDM) signalat a plurality of time intervals transmitted by the user equipment fromthe plurality of antennas. A channel in a frequency domain is calculatedin response to the plurality of subcarrier signals. A frequency-offsetestimation is calculated. The frequency-offset estimation is applied tothe channel in the frequency domain to obtain a frequency-offsetcompensated channel in the frequency domain. The frequency-offsetcompensated channel in the frequency domain is transformed to a channelin a time domain. A time-offset estimation is calculated. Thetime-offset estimation to the channel in the time domain is applied toobtain a time-offset compensated channel in the time domain. Thetime-offset compensated channel in the time domain is vectorized toobtain a vector of the time-offset compensated channel in the timedomain. The vector is correlated to obtain a spatial-time correlation.

Clause 9. The computer-implemented method of clause 8, wherein theplurality of subcarrier signals include a plurality of soundingreference signals in a resource block transmitted by the user equipment.

Clause 10. The computer-implemented method of any one of the clauses8-9, wherein calculating the frequency-offset estimation includescalculating the frequency-offset estimation based on an OFDM symbolindex.

Clause 11. The computer-implemented method of any one of the clauses8-10, wherein correlating the vector to obtain a spatial-timecorrelation includes correlating the vector to obtain an Eigendecomposition of the spatial time correlation with or without an averagepower value.

Clause 12. The computer-implemented method of any one of the clauses8-11, further comprising: obtaining a geographical location for the userequipment; and storing the spatial-time correlation or Eigendecomposition of spatial-time correlation, with or without an averagepower value associated with the geographical location in a databasestored in non-transitory memory.

Clause 13. The computer-implemented method of any one of the clauses8-12, further comprising: retrieving the spatial-time correlation orEigen decomposition of spatial-time correlation, with or without anaverage power value associated with the geographical location; and usingthe spatial-time correlation or Eigen decomposition of spatial-timecorrelation, with or without the average power value in a base stationapplication that includes one of a user handover, radio resourcemanagement, link adaptation and channel estimation.

Clause 14. The computer-implemented method of any one of the clauses8-13, wherein calculating the frequency-offset estimation is based on anOFDM symbol period and wherein calculating the time-offset estimation isbased on a subcarrier spacing in an OFDM signal.

Clause 15. A non-transitory computer-readable medium storing computerinstructions, that when executed by one or more processors, cause one ormore processors to: receive a plurality of subcarrier signals in anorthogonal frequency-division multiplexing (OFDM) signal at a pluralitytime intervals transmitted by a user equipment from a plurality ofantennas at a base station in a cellular network. A channel in afrequency domain is calculated in response to the plurality ofsubcarrier signals. A frequency-offset estimation is calculated. Atime-offset estimation is calculated. The frequency-offset estimationand the time-offset estimation to the channel in the frequency domain isapplied to obtain a time-frequency-offset compensated channel in thefrequency domain. The time-frequency-offset compensated channel in atime domain is vectorized to obtain a vector of thetime-frequency-offset compensated channel in the time domain. The vectoris correlated to obtain a spatial-frequency correlation.

Clause 16. The non-transitory computer-readable medium of clause 15,wherein correlating the vector to obtain a spatial-frequency correlationincludes correlating the vector to obtain an Eigen decomposition of thespatial-frequency correlation with or without an average power value

Clause 17. The non-transitory computer-readable medium of any one of theclauses 15-16, further comprising computer instructions causing one ormore processors to: obtaining a geographical location for the userequipment; and storing the spatial-frequency correlation or Eigendecomposition of the spatial-time correlation, with or without theaverage power value associated with the geographical location in adatabase stored in non-transitory memory.

Clause 18. The non-transitory computer-readable medium of any one of theclauses 15-17, further comprising computer instructions causing one ormore processors to: retrieving the spatial-frequency correlation orEigen decomposition of the spatial-time correlation, with or without theaverage power value associated with the geographical location; and usingthe spatial-frequency correlation or Eigen decomposition of thespatial-time correlation, with or without the average power value in abase station application that includes one of a user handover, radioresource management, link adaptation and channel estimation.

Clause 19. The non-transitory computer-readable medium of any one of theclauses 15-18, wherein calculating the frequency-offset estimation isbased on an OFDM symbol period and calculating the time-offsetestimation is based on an OFDM subcarrier spacing.

Clause 20. The non-transitory computer-readable medium of any one of theclauses 15-19, wherein the vectorizing is based on a number of frequencydomain channel sampling points.

The terminology used herein is for the purpose of describing particularaspects only and is not intended to be limiting of the disclosure. Asused herein, the singular forms “a”, “an” and “the” are intended toinclude the plural forms as well, unless the context clearly indicatesotherwise. It will be further understood that the terms “comprises”and/or “comprising,” when used in this specification, specify thepresence of stated features, integers, steps, operations, elements,and/or components, but do not preclude the presence or addition of oneor more other features, integers, steps, operations, elements,components, and/or groups thereof.

It is understood that the present subject matter may be embodied in manydifferent forms and should not be construed as being limited to theembodiments set forth herein. Rather, these embodiments are provided sothat this subject matter will be thorough and complete and will fullyconvey the disclosure to those skilled in the art. Indeed, the subjectmatter is intended to cover alternatives, modifications and equivalentsof these embodiments, which are included within the scope and spirit ofthe subject matter as defined by the appended claims. Furthermore, inthe detailed description of the present subject matter, numerousspecific details are set forth in order to provide a thoroughunderstanding of the present subject matter. However, it will be clearto those of ordinary skill in the art that the present subject mattermay be practiced without such specific details.

Although the subject matter has been described in language specific tostructural features and/or methodological steps, it is to be understoodthat the subject matter defined in the appended claims is notnecessarily limited to the specific features or steps (acts) describedabove. Rather, the specific features and steps described above aredisclosed as example forms of implementing the claims.

What is claimed is:
 1. A device, comprising: a non-transitory memorystoring instructions; and one or more processors in communication withthe non-transitory memory, wherein the one or more processors executethe instructions to: obtain a plurality of received signals transmittedby a user equipment from a plurality of antenna elements in a cellularnetwork; calculate a plurality of complex channel values in an angledomain for a horizontal arrival angle and a vertical arrival angle per areceived ray in a plurality of received rays in response to theplurality of received signals, and calculate an expected value of theplurality of channel values to obtain a power value for the horizontalarrival angle and vertical arrival angle having a time delay per thereceived ray.
 2. The device of claim 1, wherein the plurality of complexchannel values for a horizontal arrival angle and a vertical arrivalangle per a received ray having a delay per received ray is compensatedwith a time-offset estimate for the plurality of complex channel values.3. The device of claim 2, wherein a time-offset estimate for theplurality of complex channel values is calculated as the smallest timedelay of the plurality of channel values in time domain.
 4. The deviceof claim 1, wherein obtain the plurality of received signals includeobtain a plurality of orthogonal frequency-division multiplexing (OFDM)signals and the plurality of antenna elements are included in amultiple-input and multiple-output (MIMO) antenna.
 5. The device ofclaim 4, wherein the plurality of OFDM signals include a plurality ofsounding reference signals in a plurality of subcarrier signals of aresource block transmitted by the user equipment.
 6. The device of claim5, wherein calculate a plurality of complex channel values in the angledomain includes: obtain an angle domain-based channel estimation andproviding the plurality of received signals to the angle domain-basedchannel to obtain the plurality of complex channel values.
 7. The deviceof claim 6, wherein obtain the angle domain-based channel estimationincludes using an array signal processing that includes one of N-pointdiscrete Fourier transform (DFT) steering, minimum variancedistortionless response (MVDR) and multiple signal classification(MUSIC).
 8. The device of claim 1, wherein the one or more processorsexecute the instructions to: obtain a geographical location for the userequipment and store the power value, the horizontal arrival angle,vertical arrival angle and a second time delay per the received ray forthe geographical location in another non-transitory memory.
 9. Thedevice of claim 8, wherein the device is included in a base stationhaving the plurality of antenna elements for communicating with the userequipment in the cellular network, wherein the one or more processorsexecute the instructions to: retrieve the power value, the horizontalarrival angle, vertical arrival angle and a second time delay per thereceived ray for the geographical location to use in one of userhandover, radio resource management, link adaptation and channelestimation in the base station.
 10. A computer-implemented method for abase station having a plurality of antennas to communicate with a userequipment in a cellular network, comprising: receiving a plurality ofsubcarrier signals in an orthogonal frequency-division multiplexing(OFDM) signal at a plurality of time intervals transmitted by the userequipment from the plurality of antennas; calculating the channel valuesin a frequency domain in response to the plurality of subcarriersignals; vectorizing the channel in the time domain to obtain a vectorof the channel in the time domain; and correlating the vector to obtaina spatial-time correlation.
 11. The device of claim 10, wherein thechannel in the frequency domain is compensated by applying thetime-offset estimated in the time domain.
 12. The device of claim 11,wherein the time-offset estimation is calculated with channels obtainedfrom received subcarrier signals.
 13. The computer-implemented method ofclaim 10, wherein the plurality of subcarrier signals include aplurality of sounding reference signals in a resource block transmittedby the user equipment.
 14. The computer-implemented method of claim 13,wherein calculating the frequency-offset estimation includes calculatingthe frequency-offset estimation based on an OFDM symbol index.
 15. Thecomputer-implemented method of claim 14, wherein correlating the vectorto obtain a spatial-time correlation includes correlating the vector toobtain an Eigen decomposition of the spatial time correlation with orwithout an average power value.
 16. The computer-implemented method ofclaim 15, further comprising: obtaining a geographical location for theuser equipment; and storing the spatial-time correlation or Eigendecomposition of spatial-time correlation, with or without an averagepower value associated with the geographical location in a databasestored in non-transitory memory.
 17. The computer-implemented method ofclaim 16, further comprising: retrieving the spatial-time correlation orEigen decomposition of spatial-time correlation, with or without anaverage power value associated with the geographical location; and usingthe spatial-time correlation or Eigen decomposition of spatial-timecorrelation, with or without the average power value in a base stationapplication that includes one of a user handover, radio resourcemanagement, link adaptation and channel estimation.
 18. Thecomputer-implemented method of claim 14, wherein calculating thefrequency-offset estimation is based on an OFDM symbol period andwherein calculating the time-offset estimation is based on a subcarrierspacing in an OFDM signal.
 19. A non-transitory computer-readable mediumstoring computer instructions, that when executed by one or moreprocessors, cause one or more processors to: receiving a plurality ofsubcarrier signals in an orthogonal frequency-division multiplexing(OFDM) signal at a plurality of time intervals transmitted by a userequipment from a plurality of antennas at a base station in a cellularnetwork; calculating channel values in a frequency domain in response tothe plurality of subcarrier signals; vectorizing the channel infrequency domain to obtain a vector of the channel in the frequencydomain; and correlating the vector to obtain a spatial-frequencycorrelation.
 20. The device of claim 19, wherein the channel in thefrequency domain is compensated by applying the time-offset estimated inthe time domain.
 21. The device of claim 20, wherein the time-offsetestimation is calculated with channels obtained from received subcarriersignals.
 22. The non-transitory computer-readable medium of claim 19,wherein correlating the vector to obtain a spatial-frequency correlationincludes correlating the vector to obtain an Eigen decomposition of thespatial-frequency correlation with or without an average power value.23. The non-transitory computer-readable medium of claim 22, furthercomprising computer instructions causing one or more processors to:obtaining a geographical location for the user equipment; and storingthe spatial-frequency correlation or Eigen decomposition of thespatial-time correlation, with or without the average power valueassociated with the geographical location in a database stored innon-transitory memory.
 24. The non-transitory computer-readable mediumof claim 23, further comprising computer instructions causing one ormore processors to: retrieving the spatial-frequency correlation orEigen decomposition of the spatial-time correlation, with or without theaverage power value associated with the geographical location; and usingthe spatial-frequency correlation or Eigen decomposition of thespatial-time correlation, with or without the average power value in abase station application that includes one of a user handover, radioresource management, link adaptation and channel estimation.
 25. Thenon-transitory computer-readable medium of claim 19, wherein calculatingthe frequency-offset estimation is based on an OFDM symbol period andcalculating the time-offset estimation is based on an OFDM subcarrierspacing.
 26. The non-transitory computer-readable medium of claim 19,wherein the vectorizing is based on a number of frequency domain channelsampling points.
 27. A device, comprising: a non-transitory memorystoring instructions; and one or more processors in communication withthe non-transitory memory, wherein the one or more processors executethe instructions to: obtain a plurality of received signals transmittedby a user equipment from a plurality of antenna elements in a cellularnetwork; calculate a plurality of complex channel values as raw channelestimate in a frequency domain in response to the plurality ofsubcarrier signals; and filter the raw channel estimates with powerangle delay profiles, spatial time correlation, or spatial frequencycorrelation to obtain improved channel estimates.
 28. The device ofclaim 27, wherein the power angle delay profiles are a plurality ofreceived rays with each ray represented by a power value, a horizontalarrival angle and vertical arrival angle, a time delay per the receivedray that are obtained by the measurement of the channel values inprevious time slots.
 29. The device of claim 27, wherein the spatialtime correlation is the correlating of the vectorized channel in spatialand time domain from the channel values in previous time slots.
 30. Thedevice of claim 27, wherein the spatial frequency correlation is thecorrelating of the vectorized channel in spatial and frequency domainfrom the channel values in previous time slots.
 31. Acomputer-implemented method for a base station having a plurality ofantennas to communicate with a user equipment in a cellular network,comprising: receiving a plurality of subcarrier signals in an orthogonalfrequency-division multiplexing (OFDM) signal at a plurality of timeintervals transmitted by the user equipment from the plurality ofantennas; calculating the channel values in a frequency domain inresponse to the plurality of subcarrier signals; transforming thechannel values to the time domain; generating a pseudo pilot sequence inthe time domain; obtaining the convolutional signal between the channelvalues and the pseudo pilot sequence for each antennal; vectorizing theconvolutional signal in the time domain for all antennas to obtain avector of the convolutional signal in the spatial time domain; andcorrelating the vector to obtain a spatial-time correlation.
 32. Thedevice of claim 31, wherein the pseudo pilot sequence consists of aplurality of pilot symbols.
 33. The device of claim 31, wherein thepseudo pilot sequence can be a predefined sequence.
 34. The device ofclaim 32, wherein the pilot symbols of the pseudo pilot sequence haveequal power.
 35. A computer-implemented method for a base station havinga plurality of antennas to communicate with a user equipment in acellular network, comprising: receiving a plurality of subcarriersignals in an orthogonal frequency-division multiplexing (OFDM) signalat a plurality of time intervals transmitted by the user equipment fromthe plurality of antennas; calculating the channel values in a frequencydomain in response to the plurality of subcarrier signals; generating apseudo pilot sequence in the frequency domain; obtaining themultiplication signal by multiplying each symbol of the pseudo pilotsequence in the frequency domain to the channel value on thecorresponding subcarrier; transforming the multiplication signal in thefrequency domain to the time domain; vectorizing the transformedmultiplication signal in time domain for all antennas to obtain a vectorof the signal in the spatial time domain; and correlating the vector toobtain a spatial-time correlation.
 36. The device of claim 35, whereinthe pseudo pilot sequence consists of a plurality of pilot symbols. 37.The device of claim 35, wherein the pseudo pilot sequence can be apredefined sequence.
 38. The device of claim 35, wherein the pseudopilot sequence can be generated in time domain and transformed to thefrequency domain.
 39. The device of claim 38, wherein the pilot symbolsof the pseudo pilot sequence in time domain have equal power.